Overview

Dataset statistics

Number of variables16
Number of observations3488021
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory379.2 MiB
Average record size in memory114.0 B

Variable types

Categorical7
DateTime2
Numeric7

Alerts

ride_id has a high cardinality: 3488021 distinct values High cardinality
start_station_name has a high cardinality: 1583 distinct values High cardinality
start_station_id has a high cardinality: 1576 distinct values High cardinality
end_station_name has a high cardinality: 1623 distinct values High cardinality
end_station_id has a high cardinality: 1616 distinct values High cardinality
start_lat is highly correlated with start_lng and 2 other fieldsHigh correlation
start_lng is highly correlated with start_lat and 2 other fieldsHigh correlation
end_lat is highly correlated with start_lat and 2 other fieldsHigh correlation
end_lng is highly correlated with start_lat and 2 other fieldsHigh correlation
start_hour is highly correlated with end_hourHigh correlation
end_hour is highly correlated with start_hourHigh correlation
elapsed_min is highly skewed (γ1 = 300.4039448) Skewed
ride_id is uniformly distributed Uniform
ride_id has unique values Unique
start_hour has 61065 (1.8%) zeros Zeros
end_hour has 67813 (1.9%) zeros Zeros
elapsed_min has 98098 (2.8%) zeros Zeros

Reproduction

Analysis started2022-10-26 19:05:16.495017
Analysis finished2022-10-26 19:17:22.295625
Duration12 minutes and 5.8 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

ride_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3488021
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
C09E4093905089BD
 
1
04AE80D0289D063D
 
1
A1CD31FF5782B541
 
1
B16345B4B74C0BE2
 
1
9CB0536433FEACA3
 
1
Other values (3488016)
3488016 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters55808336
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3488021 ?
Unique (%)100.0%

Sample

1st rowC09E4093905089BD
2nd row374630DB5822C392
3rd row4F73CA25880A1215
4th rowECD6EE19C0CC1D31
5th row44D0987673B9997D

Common Values

ValueCountFrequency (%)
C09E4093905089BD1
 
< 0.1%
04AE80D0289D063D1
 
< 0.1%
A1CD31FF5782B5411
 
< 0.1%
B16345B4B74C0BE21
 
< 0.1%
9CB0536433FEACA31
 
< 0.1%
F5F294CDBED71D801
 
< 0.1%
11316B07F77DDC801
 
< 0.1%
4964DAD0F44C034D1
 
< 0.1%
06AD757F7966A1141
 
< 0.1%
1ADAA8874400E4191
 
< 0.1%
Other values (3488011)3488011
> 99.9%

Length

2022-10-26T14:17:23.366686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c09e4093905089bd1
 
< 0.1%
f92a7c3a29e3236e1
 
< 0.1%
0785828363ee99481
 
< 0.1%
7de97aa765e2dacb1
 
< 0.1%
4f73ca25880a12151
 
< 0.1%
ecd6ee19c0cc1d311
 
< 0.1%
44d0987673b9997d1
 
< 0.1%
a80f03b56110aff81
 
< 0.1%
d967c4fdf71ade611
 
< 0.1%
62da916392de2a171
 
< 0.1%
Other values (3488011)3488011
> 99.9%

Most occurring characters

ValueCountFrequency (%)
E3490690
 
6.3%
43490070
 
6.3%
A3489063
 
6.3%
23488979
 
6.3%
C3488571
 
6.3%
D3488555
 
6.3%
B3488357
 
6.3%
73488290
 
6.3%
03488146
 
6.3%
53487967
 
6.2%
Other values (6)20919648
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number34875951
62.5%
Uppercase Letter20932385
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
43490070
10.0%
23488979
10.0%
73488290
10.0%
03488146
10.0%
53487967
10.0%
13487372
10.0%
83487254
10.0%
33486776
10.0%
93486594
10.0%
63484503
10.0%
Uppercase Letter
ValueCountFrequency (%)
E3490690
16.7%
A3489063
16.7%
C3488571
16.7%
D3488555
16.7%
B3488357
16.7%
F3487149
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common34875951
62.5%
Latin20932385
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
43490070
10.0%
23488979
10.0%
73488290
10.0%
03488146
10.0%
53487967
10.0%
13487372
10.0%
83487254
10.0%
33486776
10.0%
93486594
10.0%
63484503
10.0%
Latin
ValueCountFrequency (%)
E3490690
16.7%
A3489063
16.7%
C3488571
16.7%
D3488555
16.7%
B3488357
16.7%
F3487149
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII55808336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E3490690
 
6.3%
43490070
 
6.3%
A3489063
 
6.3%
23488979
 
6.3%
C3488571
 
6.3%
D3488555
 
6.3%
B3488357
 
6.3%
73488290
 
6.3%
03488146
 
6.3%
53487967
 
6.2%
Other values (6)20919648
37.5%

rideable_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
classic_bike
2623429 
electric_bike
826904 
docked_bike
 
37688

Length

Max length13
Median length12
Mean length12.22626469
Min length11

Characters and Unicode

Total characters42645468
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclassic_bike
2nd rowelectric_bike
3rd rowelectric_bike
4th rowelectric_bike
5th rowclassic_bike

Common Values

ValueCountFrequency (%)
classic_bike2623429
75.2%
electric_bike826904
 
23.7%
docked_bike37688
 
1.1%

Length

2022-10-26T14:17:23.559254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-26T14:17:24.016400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
classic_bike2623429
75.2%
electric_bike826904
 
23.7%
docked_bike37688
 
1.1%

Most occurring characters

ValueCountFrequency (%)
c6938354
16.3%
i6938354
16.3%
s5246858
12.3%
e5179517
12.1%
k3525709
8.3%
_3488021
8.2%
b3488021
8.2%
l3450333
8.1%
a2623429
 
6.2%
t826904
 
1.9%
Other values (3)939968
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39157447
91.8%
Connector Punctuation3488021
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c6938354
17.7%
i6938354
17.7%
s5246858
13.4%
e5179517
13.2%
k3525709
9.0%
b3488021
8.9%
l3450333
8.8%
a2623429
 
6.7%
t826904
 
2.1%
r826904
 
2.1%
Other values (2)113064
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_3488021
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39157447
91.8%
Common3488021
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
c6938354
17.7%
i6938354
17.7%
s5246858
13.4%
e5179517
13.2%
k3525709
9.0%
b3488021
8.9%
l3450333
8.8%
a2623429
 
6.7%
t826904
 
2.1%
r826904
 
2.1%
Other values (2)113064
 
0.3%
Common
ValueCountFrequency (%)
_3488021
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42645468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c6938354
16.3%
i6938354
16.3%
s5246858
12.3%
e5179517
12.1%
k3525709
8.3%
_3488021
8.2%
b3488021
8.2%
l3450333
8.1%
a2623429
 
6.2%
t826904
 
1.9%
Other values (3)939968
 
2.2%
Distinct1671390
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
Minimum2022-07-01 00:00:01
Maximum2022-07-31 23:59:52
2022-10-26T14:17:24.323980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:17:24.550380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1673829
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
Minimum2022-07-01 00:01:32
Maximum2022-08-03 22:17:53
2022-10-26T14:17:24.807537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:17:25.121378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

start_station_name
Categorical

HIGH CARDINALITY

Distinct1583
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
West St & Chambers St
 
15865
W 21 St & 6 Ave
 
13493
Broadway & W 58 St
 
12765
Broadway & E 14 St
 
12645
6 Ave & W 33 St
 
12605
Other values (1578)
3420648 

Length

Max length45
Median length37
Mean length20.05504669
Min length9

Characters and Unicode

Total characters69952424
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMelrose St & Broadway
2nd rowE 68 St & 3 Ave
3rd rowW 37 St & 10 Ave
4th rowW 37 St & 10 Ave
5th rowE 68 St & 3 Ave

Common Values

ValueCountFrequency (%)
West St & Chambers St15865
 
0.5%
W 21 St & 6 Ave13493
 
0.4%
Broadway & W 58 St12765
 
0.4%
Broadway & E 14 St12645
 
0.4%
6 Ave & W 33 St12605
 
0.4%
12 Ave & W 40 St12339
 
0.4%
West St & Liberty St11961
 
0.3%
Broadway & W 25 St11774
 
0.3%
1 Ave & E 68 St11312
 
0.3%
E 33 St & 1 Ave11166
 
0.3%
Other values (1573)3362096
96.4%

Length

2022-10-26T14:17:25.382826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st3719173
19.1%
3417269
17.6%
ave2248755
 
11.6%
w1013545
 
5.2%
e896906
 
4.6%
broadway235860
 
1.2%
park205452
 
1.1%
pl186167
 
1.0%
6177520
 
0.9%
1171903
 
0.9%
Other values (817)7187863
36.9%

Most occurring characters

ValueCountFrequency (%)
15979954
22.8%
t5285064
 
7.6%
e4910917
 
7.0%
S4079204
 
5.8%
&3431008
 
4.9%
v2652700
 
3.8%
A2533500
 
3.6%
r2525197
 
3.6%
a2487933
 
3.6%
n2138419
 
3.1%
Other values (59)23928528
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31892520
45.6%
Space Separator15979954
22.8%
Uppercase Letter12776313
18.3%
Decimal Number5789488
 
8.3%
Other Punctuation3471456
 
5.0%
Open Punctuation14379
 
< 0.1%
Close Punctuation14379
 
< 0.1%
Dash Punctuation13465
 
< 0.1%
Control470
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5285064
16.6%
e4910917
15.4%
v2652700
8.3%
r2525197
7.9%
a2487933
7.8%
n2138419
 
6.7%
o1912414
 
6.0%
l1400313
 
4.4%
s1214941
 
3.8%
i1214542
 
3.8%
Other values (16)6150080
19.3%
Uppercase Letter
ValueCountFrequency (%)
S4079204
31.9%
A2533500
19.8%
W1379583
 
10.8%
E983213
 
7.7%
B620559
 
4.9%
P611115
 
4.8%
C503276
 
3.9%
M329771
 
2.6%
G238120
 
1.9%
L212428
 
1.7%
Other values (14)1285544
 
10.1%
Decimal Number
ValueCountFrequency (%)
11312380
22.7%
2707925
12.2%
3642804
11.1%
4567095
9.8%
5539356
9.3%
6504794
 
8.7%
8451669
 
7.8%
7407833
 
7.0%
0383200
 
6.6%
9272432
 
4.7%
Other Punctuation
ValueCountFrequency (%)
&3431008
98.8%
\27204
 
0.8%
.9935
 
0.3%
'3309
 
0.1%
Space Separator
ValueCountFrequency (%)
15979954
100.0%
Open Punctuation
ValueCountFrequency (%)
(14379
100.0%
Close Punctuation
ValueCountFrequency (%)
)14379
100.0%
Dash Punctuation
ValueCountFrequency (%)
-13465
100.0%
Control
ValueCountFrequency (%)
470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44668833
63.9%
Common25283591
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5285064
 
11.8%
e4910917
 
11.0%
S4079204
 
9.1%
v2652700
 
5.9%
A2533500
 
5.7%
r2525197
 
5.7%
a2487933
 
5.6%
n2138419
 
4.8%
o1912414
 
4.3%
l1400313
 
3.1%
Other values (40)14743172
33.0%
Common
ValueCountFrequency (%)
15979954
63.2%
&3431008
 
13.6%
11312380
 
5.2%
2707925
 
2.8%
3642804
 
2.5%
4567095
 
2.2%
5539356
 
2.1%
6504794
 
2.0%
8451669
 
1.8%
7407833
 
1.6%
Other values (9)738773
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII69952424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15979954
22.8%
t5285064
 
7.6%
e4910917
 
7.0%
S4079204
 
5.8%
&3431008
 
4.9%
v2652700
 
3.8%
A2533500
 
3.6%
r2525197
 
3.6%
a2487933
 
3.6%
n2138419
 
3.1%
Other values (59)23928528
34.2%

start_station_id
Categorical

HIGH CARDINALITY

Distinct1576
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
5329.03
 
15865
6140.05
 
13493
6948.10
 
12765
5905.12
 
12645
6364.07
 
12605
Other values (1571)
3420648 

Length

Max length9
Median length7
Mean length6.999991973
Min length6

Characters and Unicode

Total characters24416119
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4801.04
2nd row6896.16
3rd row6611.02
4th row6611.02
5th row6896.16

Common Values

ValueCountFrequency (%)
5329.0315865
 
0.5%
6140.0513493
 
0.4%
6948.1012765
 
0.4%
5905.1212645
 
0.4%
6364.0712605
 
0.4%
6765.0112339
 
0.4%
5184.0811961
 
0.3%
6173.0811774
 
0.3%
6822.0911312
 
0.3%
6197.0811166
 
0.3%
Other values (1566)3362096
96.4%

Length

2022-10-26T14:17:25.632499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5329.0315865
 
0.5%
6140.0513493
 
0.4%
6948.1012765
 
0.4%
5905.1212645
 
0.4%
6364.0712605
 
0.4%
6765.0112339
 
0.4%
5184.0811961
 
0.3%
6173.0811774
 
0.3%
6822.0911312
 
0.3%
6197.0811166
 
0.3%
Other values (1568)3362100
96.4%

Most occurring characters

ValueCountFrequency (%)
04041603
16.6%
.3487987
14.3%
52541707
10.4%
62519288
10.3%
12213222
9.1%
41964112
8.0%
71882604
7.7%
21585712
 
6.5%
31479184
 
6.1%
81410884
 
5.8%
Other values (10)1289816
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20928018
85.7%
Other Punctuation3487987
 
14.3%
Uppercase Letter104
 
< 0.1%
Space Separator4
 
< 0.1%
Lowercase Letter4
 
< 0.1%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04041603
19.3%
52541707
12.1%
62519288
12.0%
12213222
10.6%
41964112
9.4%
71882604
9.0%
21585712
 
7.6%
31479184
 
7.1%
81410884
 
6.7%
91289702
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S64
61.5%
Y34
32.7%
L2
 
1.9%
N2
 
1.9%
C2
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
a2
50.0%
b2
50.0%
Other Punctuation
ValueCountFrequency (%)
.3487987
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24416011
> 99.9%
Latin108
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
04041603
16.6%
.3487987
14.3%
52541707
10.4%
62519288
10.3%
12213222
9.1%
41964112
8.0%
71882604
7.7%
21585712
 
6.5%
31479184
 
6.1%
81410884
 
5.8%
Other values (3)1289708
 
5.3%
Latin
ValueCountFrequency (%)
S64
59.3%
Y34
31.5%
L2
 
1.9%
a2
 
1.9%
b2
 
1.9%
N2
 
1.9%
C2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII24416119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04041603
16.6%
.3487987
14.3%
52541707
10.4%
62519288
10.3%
12213222
9.1%
41964112
8.0%
71882604
7.7%
21585712
 
6.5%
31479184
 
6.1%
81410884
 
5.8%
Other values (10)1289816
 
5.3%

end_station_name
Categorical

HIGH CARDINALITY

Distinct1623
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
West St & Chambers St
 
15955
W 21 St & 6 Ave
 
13548
Broadway & E 14 St
 
12798
12 Ave & W 40 St
 
12732
Broadway & W 58 St
 
12149
Other values (1618)
3420839 

Length

Max length45
Median length40
Mean length20.06063782
Min length8

Characters and Unicode

Total characters69971926
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowMyrtle Ave & Grove St
2nd rowE 85 St & York Ave
3rd rowKnickerbocker Ave & Cooper St
4th row6 Ave & Broome St
5th rowE 66 St & Madison Ave

Common Values

ValueCountFrequency (%)
West St & Chambers St15955
 
0.5%
W 21 St & 6 Ave13548
 
0.4%
Broadway & E 14 St12798
 
0.4%
12 Ave & W 40 St12732
 
0.4%
Broadway & W 58 St12149
 
0.3%
6 Ave & W 33 St12109
 
0.3%
West St & Liberty St11997
 
0.3%
Broadway & W 25 St11803
 
0.3%
1 Ave & E 68 St11320
 
0.3%
10 Ave & W 14 St11274
 
0.3%
Other values (1613)3362336
96.4%

Length

2022-10-26T14:17:25.989078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st3723134
19.1%
3416295
17.6%
ave2243227
 
11.5%
w1003642
 
5.2%
e894450
 
4.6%
broadway233565
 
1.2%
park205400
 
1.1%
pl187321
 
1.0%
6176739
 
0.9%
1172671
 
0.9%
Other values (856)7193396
37.0%

Most occurring characters

ValueCountFrequency (%)
15969437
22.8%
t5296037
 
7.6%
e4917230
 
7.0%
S4085017
 
5.8%
&3429985
 
4.9%
v2648440
 
3.8%
r2532947
 
3.6%
A2527857
 
3.6%
a2488951
 
3.6%
n2145475
 
3.1%
Other values (60)23930550
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31946617
45.7%
Space Separator15969437
22.8%
Uppercase Letter12779354
18.3%
Decimal Number5764381
 
8.2%
Other Punctuation3470523
 
5.0%
Open Punctuation13791
 
< 0.1%
Close Punctuation13791
 
< 0.1%
Dash Punctuation13586
 
< 0.1%
Control446
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5296037
16.6%
e4917230
15.4%
v2648440
8.3%
r2532947
7.9%
a2488951
7.8%
n2145475
 
6.7%
o1916320
 
6.0%
l1408503
 
4.4%
i1219050
 
3.8%
s1215386
 
3.8%
Other values (16)6158278
19.3%
Uppercase Letter
ValueCountFrequency (%)
S4085017
32.0%
A2527857
19.8%
W1371090
 
10.7%
E979514
 
7.7%
B621030
 
4.9%
P613934
 
4.8%
C504748
 
3.9%
M329569
 
2.6%
G238832
 
1.9%
L213395
 
1.7%
Other values (15)1294368
 
10.1%
Decimal Number
ValueCountFrequency (%)
11312924
22.8%
2706773
12.3%
3637460
11.1%
4565198
9.8%
5535168
9.3%
6500442
 
8.7%
8448096
 
7.8%
7404593
 
7.0%
0381858
 
6.6%
9271869
 
4.7%
Other Punctuation
ValueCountFrequency (%)
&3429985
98.8%
\27276
 
0.8%
.9868
 
0.3%
'3394
 
0.1%
Space Separator
ValueCountFrequency (%)
15969437
100.0%
Open Punctuation
ValueCountFrequency (%)
(13791
100.0%
Close Punctuation
ValueCountFrequency (%)
)13791
100.0%
Dash Punctuation
ValueCountFrequency (%)
-13586
100.0%
Control
ValueCountFrequency (%)
446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44725971
63.9%
Common25245955
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5296037
 
11.8%
e4917230
 
11.0%
S4085017
 
9.1%
v2648440
 
5.9%
r2532947
 
5.7%
A2527857
 
5.7%
a2488951
 
5.6%
n2145475
 
4.8%
o1916320
 
4.3%
l1408503
 
3.1%
Other values (41)14759194
33.0%
Common
ValueCountFrequency (%)
15969437
63.3%
&3429985
 
13.6%
11312924
 
5.2%
2706773
 
2.8%
3637460
 
2.5%
4565198
 
2.2%
5535168
 
2.1%
6500442
 
2.0%
8448096
 
1.8%
7404593
 
1.6%
Other values (9)735879
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII69971926
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15969437
22.8%
t5296037
 
7.6%
e4917230
 
7.0%
S4085017
 
5.8%
&3429985
 
4.9%
v2648440
 
3.8%
r2532947
 
3.6%
A2527857
 
3.6%
a2488951
 
3.6%
n2145475
 
3.1%
Other values (60)23930550
34.2%

end_station_id
Categorical

HIGH CARDINALITY

Distinct1616
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
5329.03
 
15955
6140.05
 
13548
5905.12
 
12798
6765.01
 
12732
6948.10
 
12149
Other values (1611)
3420839 

Length

Max length9
Median length7
Mean length6.999870987
Min length5

Characters and Unicode

Total characters24415697
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row4816.05
2nd row7146.04
3rd row4582.05
4th row5610.09
5th row6969.08

Common Values

ValueCountFrequency (%)
5329.0315955
 
0.5%
6140.0513548
 
0.4%
5905.1212798
 
0.4%
6765.0112732
 
0.4%
6948.1012149
 
0.3%
6364.0712109
 
0.3%
5184.0811997
 
0.3%
6173.0811803
 
0.3%
6822.0911320
 
0.3%
6157.0411274
 
0.3%
Other values (1606)3362336
96.4%

Length

2022-10-26T14:17:26.185212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5329.0315955
 
0.5%
6140.0513548
 
0.4%
5905.1212798
 
0.4%
6765.0112732
 
0.4%
6948.1012149
 
0.3%
6364.0712109
 
0.3%
5184.0811997
 
0.3%
6173.0811803
 
0.3%
6822.0911320
 
0.3%
6157.0411274
 
0.3%
Other values (1608)3362340
96.4%

Most occurring characters

ValueCountFrequency (%)
04042523
16.6%
.3487681
14.3%
52551383
10.4%
62509236
10.3%
12214454
9.1%
41964647
8.0%
71881208
7.7%
21585997
 
6.5%
31481140
 
6.1%
81407296
 
5.8%
Other values (13)1290132
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20927100
85.7%
Other Punctuation3487681
 
14.3%
Uppercase Letter906
 
< 0.1%
Space Separator4
 
< 0.1%
Lowercase Letter4
 
< 0.1%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04042523
19.3%
52551383
12.2%
62509236
12.0%
12214454
10.6%
41964647
9.4%
71881208
9.0%
21585997
 
7.6%
31481140
 
7.1%
81407296
 
6.7%
91289216
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S444
49.0%
Y224
24.7%
C66
 
7.3%
J64
 
7.1%
H52
 
5.7%
B52
 
5.7%
L2
 
0.2%
N2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
a2
50.0%
b2
50.0%
Other Punctuation
ValueCountFrequency (%)
.3487681
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24414787
> 99.9%
Latin910
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
04042523
16.6%
.3487681
14.3%
52551383
10.5%
62509236
10.3%
12214454
9.1%
41964647
8.0%
71881208
7.7%
21585997
 
6.5%
31481140
 
6.1%
81407296
 
5.8%
Other values (3)1289222
 
5.3%
Latin
ValueCountFrequency (%)
S444
48.8%
Y224
24.6%
C66
 
7.3%
J64
 
7.0%
H52
 
5.7%
B52
 
5.7%
L2
 
0.2%
a2
 
0.2%
b2
 
0.2%
N2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII24415697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04042523
16.6%
.3487681
14.3%
52551383
10.4%
62509236
10.3%
12214454
9.1%
41964647
8.0%
71881208
7.7%
21585997
 
6.5%
31481140
 
6.1%
81407296
 
5.8%
Other values (13)1290132
 
5.3%

start_lat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct345152
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.74105508
Minimum40.63333225
Maximum40.88240421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 MiB
2022-10-26T14:17:26.440457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum40.63333225
5-th percentile40.67818388
Q140.71534825
median40.739323
Q340.76350532
95-th percentile40.81613636
Maximum40.88240421
Range0.249071955
Interquartile range (IQR)0.04815707

Descriptive statistics

Standard deviation0.04010876516
Coefficient of variation (CV)0.0009844802761
Kurtosis0.3953671373
Mean40.74105508
Median Absolute Deviation (MAD)0.02409079
Skewness0.4357546311
Sum142105655.7
Variance0.001608713042
MonotonicityNot monotonic
2022-10-26T14:17:26.686145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7175483414545
 
0.4%
40.7417396912226
 
0.4%
40.7345456711283
 
0.3%
40.7490127111220
 
0.3%
40.7608750211112
 
0.3%
40.71144411035
 
0.3%
40.7669531710861
 
0.3%
40.7428687710286
 
0.3%
40.743226819850
 
0.3%
40.74198169782
 
0.3%
Other values (345142)3375821
96.8%
ValueCountFrequency (%)
40.633332251
< 0.1%
40.633335111
< 0.1%
40.633340361
< 0.1%
40.633344171
< 0.1%
40.633344651
< 0.1%
40.633345961
< 0.1%
40.633349181
< 0.1%
40.63335181
< 0.1%
40.63335551
< 0.1%
40.633356811
< 0.1%
ValueCountFrequency (%)
40.882404211
< 0.1%
40.882322071
< 0.1%
40.882311581
< 0.1%
40.88230551
< 0.1%
40.882294891
< 0.1%
40.882292991
< 0.1%
40.882286191
< 0.1%
40.882280471
< 0.1%
40.882276422
< 0.1%
40.882273321
< 0.1%

start_lng
Real number (ℝ)

HIGH CORRELATION

Distinct320002
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.97434177
Minimum-74.02684856
Maximum-73.88126779
Zeros0
Zeros (%)0.0%
Negative3488021
Negative (%)100.0%
Memory size26.6 MiB
2022-10-26T14:17:28.426149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-74.02684856
5-th percentile-74.00876909
Q1-73.99379969
median-73.9812206
Q3-73.95779
95-th percentile-73.918214
Maximum-73.88126779
Range0.145580769
Interquartile range (IQR)0.03600968643

Descriptive statistics

Standard deviation0.02694649222
Coefficient of variation (CV)-0.0003642680904
Kurtosis0.2384908702
Mean-73.97434177
Median Absolute Deviation (MAD)0.01618129014
Skewness0.8689945971
Sum-258024057.5
Variance0.0007261134427
MonotonicityNot monotonic
2022-10-26T14:17:28.844764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.0132206914545
 
0.4%
-73.9941555612226
 
0.4%
-73.9907414211283
 
0.3%
-73.9884839511220
 
0.3%
-74.0027766811112
 
0.3%
-74.01484711035
 
0.3%
-73.9816933310861
 
0.3%
-73.9891862910286
 
0.3%
-73.974497849850
 
0.3%
-74.00831589782
 
0.3%
Other values (319992)3375821
96.8%
ValueCountFrequency (%)
-74.026848561
 
< 0.1%
-74.026828531
 
< 0.1%
-74.026824361
 
< 0.1%
-74.026823456
< 0.1%
-74.026818511
 
< 0.1%
-74.026816491
 
< 0.1%
-74.026812551
 
< 0.1%
-74.026805161
 
< 0.1%
-74.026804571
 
< 0.1%
-74.026802781
 
< 0.1%
ValueCountFrequency (%)
-73.881267791
 
< 0.1%
-73.881433721
 
< 0.1%
-73.881445171
 
< 0.1%
-73.88145281
< 0.1%
-73.881451491
 
< 0.1%
-73.881456371
 
< 0.1%
-73.881458161
 
< 0.1%
-73.881460791
 
< 0.1%
-73.881463891
 
< 0.1%
-73.881466631
 
< 0.1%

end_lat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.74083298
Minimum40.633385
Maximum40.88226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 MiB
2022-10-26T14:17:29.229115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum40.633385
5-th percentile40.6777287
Q140.7153379
median40.73901691
Q340.76344058
95-th percentile40.815484
Maximum40.88226
Range0.248875
Interquartile range (IQR)0.04810268

Descriptive statistics

Standard deviation0.04014157449
Coefficient of variation (CV)0.0009852909612
Kurtosis0.3940662367
Mean40.74083298
Median Absolute Deviation (MAD)0.0241370879
Skewness0.4390702894
Sum142104881
Variance0.001611346002
MonotonicityNot monotonic
2022-10-26T14:17:29.661379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7175483415594
 
0.4%
40.7417396913475
 
0.4%
40.7345456712705
 
0.4%
40.7608750212281
 
0.4%
40.71144411997
 
0.3%
40.7490127111932
 
0.3%
40.7669531711869
 
0.3%
40.7428687711603
 
0.3%
40.7650052511305
 
0.3%
40.7432268111214
 
0.3%
Other values (2318)3364046
96.4%
ValueCountFrequency (%)
40.633385323
< 0.1%
40.635679464
< 0.1%
40.637033378
< 0.1%
40.6376681
 
< 0.1%
40.638196205
 
< 0.1%
40.638246244
< 0.1%
40.639421576
< 0.1%
40.639673240
< 0.1%
40.639859166
 
< 0.1%
40.63997875
 
< 0.1%
ValueCountFrequency (%)
40.88226382
< 0.1%
40.8802945137
 
< 0.1%
40.87935551
< 0.1%
40.87812218
 
< 0.1%
40.877964216
 
< 0.1%
40.87704217
 
< 0.1%
40.87656255
< 0.1%
40.875531198
 
< 0.1%
40.87444148
 
< 0.1%
40.8740704143
 
< 0.1%

end_lng
Real number (ℝ)

HIGH CORRELATION

Distinct2320
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.97437321
Minimum-74.08670068
Maximum-73.88145
Zeros0
Zeros (%)0.0%
Negative3488021
Negative (%)100.0%
Memory size26.6 MiB
2022-10-26T14:17:29.908401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-74.08670068
5-th percentile-74.00876909
Q1-73.993915
median-73.98122549
Q3-73.95779
95-th percentile-73.918214
Maximum-73.88145
Range0.2052506779
Interquartile range (IQR)0.036125

Descriptive statistics

Standard deviation0.0269830382
Coefficient of variation (CV)-0.0003647619714
Kurtosis0.2344138803
Mean-73.97437321
Median Absolute Deviation (MAD)0.01654599057
Skewness0.8667193855
Sum-258024167.2
Variance0.0007280843508
MonotonicityNot monotonic
2022-10-26T14:17:30.091483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.0132206915594
 
0.4%
-73.9941555613475
 
0.4%
-73.9907414212705
 
0.4%
-74.0027766812281
 
0.4%
-74.01484711997
 
0.3%
-73.9884839511932
 
0.3%
-73.9816933311869
 
0.3%
-73.9891862911603
 
0.3%
-73.9581849111305
 
0.3%
-73.9744978411214
 
0.3%
Other values (2310)3364046
96.4%
ValueCountFrequency (%)
-74.086700682
< 0.1%
-74.071959262
< 0.1%
-74.0714554
< 0.1%
-74.071261881
 
< 0.1%
-74.067622133
< 0.1%
-74.051788631
 
< 0.1%
-74.050443641
 
< 0.1%
-74.049967831
 
< 0.1%
-74.049637912
< 0.1%
-74.045571681
 
< 0.1%
ValueCountFrequency (%)
-73.88145392
< 0.1%
-73.88187566305
< 0.1%
-73.8818764
 
< 0.1%
-73.8833653
 
< 0.1%
-73.88336509143
 
< 0.1%
-73.88366255
< 0.1%
-73.88412234
< 0.1%
-73.884308487
< 0.1%
-73.88459193
 
< 0.1%
-73.88475503216
< 0.1%

member_casual
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
member
2653232 
casual
834789 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters20928126
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmember
2nd rowmember
3rd rowmember
4th rowmember
5th rowmember

Common Values

ValueCountFrequency (%)
member2653232
76.1%
casual834789
 
23.9%

Length

2022-10-26T14:17:30.274400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-26T14:17:30.419503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
member2653232
76.1%
casual834789
 
23.9%

Most occurring characters

ValueCountFrequency (%)
m5306464
25.4%
e5306464
25.4%
b2653232
12.7%
r2653232
12.7%
a1669578
 
8.0%
c834789
 
4.0%
s834789
 
4.0%
u834789
 
4.0%
l834789
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20928126
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m5306464
25.4%
e5306464
25.4%
b2653232
12.7%
r2653232
12.7%
a1669578
 
8.0%
c834789
 
4.0%
s834789
 
4.0%
u834789
 
4.0%
l834789
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20928126
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m5306464
25.4%
e5306464
25.4%
b2653232
12.7%
r2653232
12.7%
a1669578
 
8.0%
c834789
 
4.0%
s834789
 
4.0%
u834789
 
4.0%
l834789
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20928126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m5306464
25.4%
e5306464
25.4%
b2653232
12.7%
r2653232
12.7%
a1669578
 
8.0%
c834789
 
4.0%
s834789
 
4.0%
u834789
 
4.0%
l834789
 
4.0%

start_hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.26480316
Minimum0
Maximum23
Zeros61065
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size26.6 MiB
2022-10-26T14:17:30.547536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median15
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.262146354
Coefficient of variation (CV)0.3688902183
Kurtosis-0.1330801813
Mean14.26480316
Median Absolute Deviation (MAD)4
Skewness-0.5799520781
Sum49755933
Variance27.69018425
MonotonicityNot monotonic
2022-10-26T14:17:30.688182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18303758
 
8.7%
17301631
 
8.6%
19253148
 
7.3%
16245703
 
7.0%
15216272
 
6.2%
14207563
 
6.0%
13196836
 
5.6%
20193778
 
5.6%
12192265
 
5.5%
8176008
 
5.0%
Other values (14)1201059
34.4%
ValueCountFrequency (%)
061065
 
1.8%
138895
 
1.1%
225728
 
0.7%
316145
 
0.5%
413167
 
0.4%
524608
 
0.7%
665267
 
1.9%
7115824
3.3%
8176008
5.0%
9162926
4.7%
ValueCountFrequency (%)
2389909
 
2.6%
22121217
 
3.5%
21142570
4.1%
20193778
5.6%
19253148
7.3%
18303758
8.7%
17301631
8.6%
16245703
7.0%
15216272
6.2%
14207563
6.0%

end_hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.3728309
Minimum0
Maximum23
Zeros67813
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size26.6 MiB
2022-10-26T14:17:30.895140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median15
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.350884359
Coefficient of variation (CV)0.3722916102
Kurtosis-0.08061610459
Mean14.3728309
Median Absolute Deviation (MAD)4
Skewness-0.6286312898
Sum50132736
Variance28.63196342
MonotonicityNot monotonic
2022-10-26T14:17:31.066475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18308895
 
8.9%
17290508
 
8.3%
19264699
 
7.6%
16237117
 
6.8%
15213711
 
6.1%
20208385
 
6.0%
14203291
 
5.8%
13194851
 
5.6%
12187595
 
5.4%
9166878
 
4.8%
Other values (14)1212091
34.8%
ValueCountFrequency (%)
067813
1.9%
143351
 
1.2%
228871
 
0.8%
318338
 
0.5%
413631
 
0.4%
521384
 
0.6%
657828
 
1.7%
7103790
3.0%
8165186
4.7%
9166878
4.8%
ValueCountFrequency (%)
2398991
 
2.8%
22127860
3.7%
21153649
4.4%
20208385
6.0%
19264699
7.6%
18308895
8.9%
17290508
8.3%
16237117
6.8%
15213711
6.1%
14203291
5.8%

elapsed_min
Real number (ℝ)

SKEWED
ZEROS

Distinct1510
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.70321566
Minimum-16
Maximum43425
Zeros98098
Zeros (%)2.8%
Negative84
Negative (%)< 0.1%
Memory size26.6 MiB
2022-10-26T14:17:31.376478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile1
Q15
median10
Q319
95-th percentile41
Maximum43425
Range43441
Interquartile range (IQR)14

Descriptive statistics

Standard deviation67.22909684
Coefficient of variation (CV)4.2812312
Kurtosis145941.1132
Mean15.70321566
Median Absolute Deviation (MAD)6
Skewness300.4039448
Sum54773146
Variance4519.751462
MonotonicityNot monotonic
2022-10-26T14:17:31.652220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5204859
 
5.9%
6201035
 
5.8%
4198237
 
5.7%
7191771
 
5.5%
8179261
 
5.1%
3177728
 
5.1%
9164785
 
4.7%
10150854
 
4.3%
2141468
 
4.1%
11137509
 
3.9%
Other values (1500)1740514
49.9%
ValueCountFrequency (%)
-161
 
< 0.1%
-131
 
< 0.1%
-91
 
< 0.1%
-61
 
< 0.1%
-32
 
< 0.1%
-178
 
< 0.1%
098098
2.8%
196574
2.8%
2141468
4.1%
3177728
5.1%
ValueCountFrequency (%)
434251
< 0.1%
413961
< 0.1%
369351
< 0.1%
246151
< 0.1%
238661
< 0.1%
231951
< 0.1%
225111
< 0.1%
187171
< 0.1%
175941
< 0.1%
174251
< 0.1%

Interactions

2022-10-26T14:16:08.483406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:17.771844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:36.984728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:06.962257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:28.116923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:44.635893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:57.032171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:09.892640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:20.773164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:56.506319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:10.538001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:30.510357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:46.305570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:58.683331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:11.763928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:23.071339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:14:08.613374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:12.633367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:32.258919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:48.298034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:00.182740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:21.600175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:25.140304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:14:19.497094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:14.446952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:34.074786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:49.855553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:01.965020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:23.782613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:27.183982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:14:35.407810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:16.466978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:36.988518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:51.436023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:03.704273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:26.464623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:29.940965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:14:48.421091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:19.949146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:40.486526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:53.703950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:05.352733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:28.235913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:13:33.221659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:14:58.490993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:25.246135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:42.780070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:15:55.583421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-26T14:16:06.966876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-26T14:17:31.874173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-26T14:17:32.145463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-26T14:17:32.397712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-26T14:17:32.817494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-26T14:17:33.138538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-26T14:17:33.317120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-26T14:16:35.268443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-26T14:16:47.906378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ride_idrideable_typestarted_atended_atstart_station_namestart_station_idend_station_nameend_station_idstart_latstart_lngend_latend_lngmember_casualstart_hourend_hourelapsed_min
0C09E4093905089BDclassic_bike2022-07-23 11:34:572022-07-23 11:45:08Melrose St & Broadway4801.04Myrtle Ave & Grove St4816.0540.697481-73.93587740.699050-73.915160member111110.0
1374630DB5822C392electric_bike2022-07-29 18:19:082022-07-29 18:26:50E 68 St & 3 Ave6896.16E 85 St & York Ave7146.0440.767128-73.96224640.775369-73.948034member18187.0
24F73CA25880A1215electric_bike2022-07-16 16:30:582022-07-16 17:39:18W 37 St & 10 Ave6611.02Knickerbocker Ave & Cooper St4582.0540.756604-73.99790140.690810-73.904480member161768.0
3ECD6EE19C0CC1D31electric_bike2022-07-17 17:35:572022-07-17 18:03:36W 37 St & 10 Ave6611.026 Ave & Broome St5610.0940.756604-73.99790140.724310-74.004730member171827.0
444D0987673B9997Dclassic_bike2022-07-11 07:56:292022-07-11 07:59:15E 68 St & 3 Ave6896.16E 66 St & Madison Ave6969.0840.767128-73.96224640.768009-73.968453member772.0
5A80F03B56110AFF8classic_bike2022-07-14 19:35:532022-07-14 19:50:06Clinton Ave & Flushing Ave4762.04Bergen St & 4 Ave4322.0640.697940-73.96986840.682564-73.979898member191914.0
6D967C4FDF71ADE61classic_bike2022-07-26 20:18:172022-07-26 20:26:57E 68 St & 3 Ave6896.16E 85 St & York Ave7146.0440.767128-73.96224640.775369-73.948034member20208.0
762DA916392DE2A17electric_bike2022-07-13 06:46:502022-07-13 06:50:26E 89 St & York Ave7204.08E 85 St & York Ave7146.0440.777945-73.94604140.775369-73.948034member663.0
8DBFDF326FBAC1C0Bclassic_bike2022-07-02 11:54:212022-07-02 11:57:11E 89 St & York Ave7204.08E 85 St & York Ave7146.0440.777945-73.94604140.775369-73.948034member11112.0
95BB3497D14360353electric_bike2022-07-31 15:30:062022-07-31 15:34:5735 Ave & 37 St6563.1238 St & 30 Ave6850.0140.755733-73.92366140.764175-73.915840member15154.0

Last rows

ride_idrideable_typestarted_atended_atstart_station_namestart_station_idend_station_nameend_station_idstart_latstart_lngend_latend_lngmember_casualstart_hourend_hourelapsed_min
348801194DF6549B0B8B543electric_bike2022-07-30 06:38:382022-07-30 06:52:47W 106 St & Central Park West7606.01Grand Army Plaza & Central Park S6839.1040.798186-73.96059140.764397-73.973715member6614.0
3488012E593817D3BF47494classic_bike2022-07-20 09:56:282022-07-20 10:11:00W 59 St & 10 Ave7023.04W 51 St & Rockefeller Plaza6700.1440.770513-73.98803840.759738-73.978116member91014.0
3488013DC2D0A8E007D50A1classic_bike2022-07-07 18:28:262022-07-07 18:34:17W 59 St & 10 Ave7023.04Grand Army Plaza & Central Park S6839.1040.770513-73.98803840.764397-73.973715member18185.0
3488014D345940C40C711AEclassic_bike2022-07-20 23:21:092022-07-20 23:28:22Underhill Ave & Pacific St4231.04Adelphi St & Myrtle Ave4620.0240.680484-73.96468040.693083-73.971789member23237.0
3488015CDB429E557F530A5electric_bike2022-07-07 12:47:162022-07-07 12:55:03W 59 St & 10 Ave7023.04W 51 St & Rockefeller Plaza6700.1440.770513-73.98803840.759738-73.978116member12127.0
3488016EF571F06A5E34311docked_bike2022-07-10 14:08:112022-07-10 14:36:45W 106 St & Central Park West7606.01Grand Army Plaza & Central Park S6839.1040.798186-73.96059140.764397-73.973715casual141428.0
34880179F9F55113999F07Belectric_bike2022-07-27 01:47:522022-07-27 02:11:40Delancey St & Eldridge St5414.07Calyer St & Guernsey St5709.0340.719383-73.99147940.727558-73.955059member1223.0
3488018CBB7EF472D45EDADclassic_bike2022-07-18 19:35:342022-07-18 19:57:15Grand Concourse & East Mount Eden Ave8265.09Weeks Ave & E 175 St8340.0540.843043-73.91175340.846879-73.907342member191921.0
3488019C2176A140EBE4FB5classic_bike2022-07-25 08:13:042022-07-25 08:58:29Court St & State St4488.08E 53 St & Lexington Ave6617.0940.690147-73.99207240.758281-73.970694member8845.0
3488020405C56C8930284FDclassic_bike2022-07-08 12:18:472022-07-08 12:27:02W 59 St & 10 Ave7023.04Grand Army Plaza & Central Park S6839.1040.770513-73.98803840.764397-73.973715member12128.0